首页|Study Data from Norwegian University of Science and Technology (NTNU) Update Knowledge of Machine Learning (Agi-p: a Gender Identification Framework for Authorship Analysis Using Customized Fine-tuning of Multilingual Language Model)
Study Data from Norwegian University of Science and Technology (NTNU) Update Knowledge of Machine Learning (Agi-p: a Gender Identification Framework for Authorship Analysis Using Customized Fine-tuning of Multilingual Language Model)
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NETL
NSTL
IEEE
New research on Machine Learning is the subject of a report. According to news reporting from Alesund, Norway, by NewsRx journalists, research stated, “In this investigation, we propose a solution for the author’s gender identification task called AGI-P. This task has several real-world applications across different fields, such as marketing and advertising, forensic linguistics, sociology, recommendation systems, language processing, historical analysis, education, and language learning.” Financial support for this research came from Norwegian University of Science and Technology (NTNU), Norway. The news correspondents obtained a quote from the research from the Norwegian University of Science and Technology (NTNU), “We created a new dataset to evaluate our proposed method. The dataset is balanced in terms of gender using a random sampling method and consists of 1944 samples in total. We use accuracy as an evaluation measure and compare the performance of the proposed solution (AGI-P) against state-of-the-art machine learning classifiers and fine-tuned pre-trained multilingual language models such as DistilBERT, mBERT, XLM-RoBERTa, and Multilingual DEBERTa. In this regard, we also propose a customized fine-tuning strategy that improves the accuracy of the pre-trained language models for the author gender identification task. Our extensive experimental studies reveal that our solution (AGI-P) outperforms the well-known machine learning classifiers and fine-tuned pre-trained multilingual language models with an accuracy level of 92.03%. Moreover, the pre-trained multilingual language models, finetuned with the proposed customized strategy, outperform the fine-tuned pre-trained language models using an out-of-the-box fine-tuning strategy.”
AlesundNorwayEuropeCyborgsEmerging TechnologiesMachine LearningNorwegian University of Science and Technology (NTNU)
NETL
NSTL
IEEE
